Five explainers that build on each other. Start with the introduction if NLP is new to you.
A beginner-friendly introduction to natural language processing (NLP), covering tokenization, stemming, named entity recognition, sentiment analysis, and how modern NLP models work.
Explore how modern NLP systems measure text similarity using word embeddings, sentence transformers, and vector search to enable semantic search, duplicate detection, and recommendation systems.
A practical tutorial on building text classification systems, covering feature extraction, Naive Bayes and SVM classifiers, training data requirements, and evaluation metrics for NLP models.
How language detection algorithms work, tools for multilingual NLP, challenges of processing text in multiple languages, and best practices for building language-agnostic text analysis systems.
A technical overview of keyword extraction methods (TF-IDF, RAKE, TextRank, YAKE) and text summarization approaches (extractive vs. abstractive) with practical guidance on choosing the right technique.